The spatial frequency or frequencies of an image are the rates of pixel change per unit distance, usually expressed in cycles per degree or radian. For horizontal sampling of the gratings depicted in FIG. 3, for example, sine wave varying regions 300 and 301 both have a single spatial frequency, although the spatial frequency for region 301 is lower than the spatial frequency for region 300. Square wave varying region 302 has a number of spatial frequencies, with the lowest spatial frequency for the region 302 equal to the sole spatial frequency for region 301. Fourier analysis of image samples allows the spatial frequency or frequencies of an image to be identified.
Current digital image/video systems are designed to employ a fixed sampling frequency of at least twice the highest spatial frequency within the image (Nyquist theorem). The number of samples which are used and/or stored for an image may be reduced by sub-sampling, but such sub-sampling is not modulated by the image or video content. Moreover, while the Nyquist theorem is occasionally violated during sampling, the sampling frequency typically remains predefined and constant.
If the Nyquist theorem is met during sampling, all spatial frequencies for the sampled image are theoretically preserved. In practice, of course, uncertainties increase due to noise, with the result that reproduction of the original high spatial frequencies becomes more difficult or even impossible. Noise reduction algorithms attempt to improve recovery, which remains difficult for the highest frequencies.
There is, therefore, a need in the art for adapting the sampling frequency or sampling density according to the image or video content being sampled.